


'''simple docstring'''
import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
					from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=3							,      a_=3_2							,      a_=3							,      a_=1_0							,      a_=[8, 1_6, 3_2, 6_4]							,      a_=[1, 1, 2, 1]							,      a_=True							,      a_=True							,      a_="relu"							,      a_=3							,      a_=None							,      a_=["stage2", "stage3", "stage4"]							,      a_=[2, 3, 4]							,      a_=1							,      )    ->  Optional[Any]:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		Union[str, Any]   					=		batch_size
					lowercase						:		str   					=		image_size
					lowercase						:		Union[str, Any]   					=		num_channels
					lowercase						:		Optional[Any]   					=		embeddings_size
					lowercase						:		List[str]   					=		hidden_sizes
					lowercase						:		List[str]   					=		depths
					lowercase						:		Union[str, Any]   					=		is_training
					lowercase						:		Any   					=		use_labels
					lowercase						:		List[str]   					=		hidden_act
					lowercase						:		str   					=		num_labels
					lowercase						:		Union[str, Any]   					=		scope
					lowercase						:		Dict   					=		len(SCREAMING_SNAKE_CASE_							)
					lowercase						:		List[str]   					=		out_features
					lowercase						:		Dict   					=		out_indices
					lowercase						:		str   					=		num_groups

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Union[str, Any]   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					lowercase						:		int   					=		None
					if self.use_labels:
						lowercase						:		List[str]   					=		ids_tensor([self.batch_size]							,      self.num_labels							)

					lowercase						:		Optional[Any]   					=		self.get_config()

					return config, pixel_values, labels

				def 	a__		(     self							)    ->  int:
					return BitConfig(
					    num_channels=self.num_channels							,      embeddings_size=self.embeddings_size							,      hidden_sizes=self.hidden_sizes							,      depths=self.depths							,      hidden_act=self.hidden_act							,      num_labels=self.num_labels							,      out_features=self.out_features							,      out_indices=self.out_indices							,      num_groups=self.num_groups							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Any:
					lowercase						:		Any   					=		BitModel(config=SCREAMING_SNAKE_CASE_							)
					model.to(SCREAMING_SNAKE_CASE_							)
					model.eval()
					lowercase						:		List[str]   					=		model(SCREAMING_SNAKE_CASE_							)
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  int:
					lowercase						:		List[str]   					=		self.num_labels
					lowercase						:		Optional[int]   					=		BitForImageClassification(SCREAMING_SNAKE_CASE_							)
					model.to(SCREAMING_SNAKE_CASE_							)
					model.eval()
					lowercase						:		Dict   					=		model(SCREAMING_SNAKE_CASE_							,      labels=SCREAMING_SNAKE_CASE_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  str:
					lowercase						:		Optional[int]   					=		BitBackbone(config=SCREAMING_SNAKE_CASE_							)
					model.to(SCREAMING_SNAKE_CASE_							)
					model.eval()
					lowercase						:		Any   					=		model(SCREAMING_SNAKE_CASE_							)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps							)							,      len(config.out_features							)							)
					self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,      [self.batch_size, self.hidden_sizes[1], 4, 4]							)

					# verify channels
					self.parent.assertEqual(len(model.channels							)							,      len(config.out_features							)							)
					self.parent.assertListEqual(model.channels							,      config.hidden_sizes[1:]							)

					# verify backbone works with out_features=None
					lowercase						:		List[str]   					=		None
					lowercase						:		Dict   					=		BitBackbone(config=SCREAMING_SNAKE_CASE_							)
					model.to(SCREAMING_SNAKE_CASE_							)
					model.eval()
					lowercase						:		Any   					=		model(SCREAMING_SNAKE_CASE_							)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps							)							,      1							)
					self.parent.assertListEqual(list(result.feature_maps[0].shape							)							,      [self.batch_size, self.hidden_sizes[-1], 1, 1]							)

					# verify channels
					self.parent.assertEqual(len(model.channels							)							,      1							)
					self.parent.assertListEqual(model.channels							,      [config.hidden_sizes[-1]]							)


				def 	a__		(     self							)    ->  Any:
					lowercase						:		Optional[int]   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		List[str]   					=		config_and_inputs
					lowercase						:		Union[str, Any]   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class        _UpperCamelCase	(    lowercase__						,			lowercase__						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				_snake_case						       =				(
				    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False
				_snake_case						       =				False

				def 	a__		(     self							)    ->  Any:
					lowercase						:		List[Any]   					=		BitModelTester(self							)
					lowercase						:		List[str]   					=		ConfigTester(self							,      config_class=SCREAMING_SNAKE_CASE_							,      has_text_modality=SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  Union[str, Any]:
					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

				def 	a__		(     self							)    ->  Tuple:
					return

				@unittest.skip(reason="Bit does not output attentions"							)
				def 	a__		(     self							)    ->  Union[str, Any]:
					pass

				@unittest.skip(reason="Bit does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  Optional[int]:
					pass

				@unittest.skip(reason="Bit does not support input and output embeddings"							)
				def 	a__		(     self							)    ->  int:
					pass

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase					,		lowercase						:		Tuple   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Tuple   					=		model_class(SCREAMING_SNAKE_CASE_							)
						lowercase						:		Tuple   					=		inspect.signature(model.forward							)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						lowercase						:		Any   					=		[*signature.parameters.keys()]

						lowercase						:		Optional[Any]   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  List[Any]:
					lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase					,		lowercase						:		Any   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		int   					=		model_class(config=SCREAMING_SNAKE_CASE_							)
						for name, module in model.named_modules():
							if isinstance(SCREAMING_SNAKE_CASE_							,      (nn.BatchNormad, nn.GroupNorm)							):
								self.assertTrue(
								    torch.all(module.weight == 1							)							,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,      )
								self.assertTrue(
								    torch.all(module.bias == 0							)							,      msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,      )

				def 	a__		(     self							)    ->  Dict:


					def check_hidden_states_output(a_							,      a_							,      a_							):
						lowercase						:		Optional[int]   					=		model_class(SCREAMING_SNAKE_CASE_							)
						model.to(SCREAMING_SNAKE_CASE_							)
						model.eval()

						with torch.no_grad():
							lowercase						:		List[str]   					=		model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							)							)

						lowercase						:		List[str]   					=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase						:		Optional[Any]   					=		self.model_tester.num_stages
						self.assertEqual(len(SCREAMING_SNAKE_CASE_							)							,      expected_num_stages + 1							)

						# Bit's feature maps are of shape (batch_size, num_channels, height, width)
						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]							)							,      [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,      )

					lowercase					,		lowercase						:		str   					=		self.model_tester.prepare_config_and_inputs_for_common()
					lowercase						:		Tuple   					=		["preactivation", "bottleneck"]
					for model_class in self.all_model_classes:
						for layer_type in layers_type:
							lowercase						:		Tuple   					=		layer_type
							lowercase						:		Any   					=		True
							check_hidden_states_output(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowercase						:		Any   					=		True

							check_hidden_states_output(SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							,      SCREAMING_SNAKE_CASE_							)

				@unittest.skip(reason="Bit does not use feedforward chunking"							)
				def 	a__		(     self							)    ->  str:
					pass

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		int   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:


					for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		str   					=		BitModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
						self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)




def 	_A  (       )  ->				int:
	lowercase						:		Optional[Any]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image




@require_torch
@require_vision
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@cached_property
				def 	a__		(     self							)    ->  List[str]:
					return (
					    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							) if is_vision_available() else None
					)


				@slow
				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(SCREAMING_SNAKE_CASE_							)

					lowercase						:		Dict   					=		self.default_image_processor
					lowercase						:		Any   					=		prepare_img()
					lowercase						:		str   					=		image_processor(images=SCREAMING_SNAKE_CASE_							,      return_tensors="pt"							).to(SCREAMING_SNAKE_CASE_							)

					# forward pass
					with torch.no_grad():
						lowercase						:		Optional[int]   					=		model(**SCREAMING_SNAKE_CASE_							)

					# verify the logits
					lowercase						:		List[Any]   					=		torch.Size((1, 1_0_0_0)							)
					self.assertEqual(outputs.logits.shape							,      SCREAMING_SNAKE_CASE_							)

					lowercase						:		List[Any]   					=		torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]							).to(SCREAMING_SNAKE_CASE_							)

					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      SCREAMING_SNAKE_CASE_							,      atol=1e-4							)							)




@require_torch
class        _UpperCamelCase	(    lowercase__						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(BitBackbone,) if is_torch_available() else ()
				_snake_case						       =				BitConfig

				_snake_case						       =				False

				def 	a__		(     self							)    ->  str:
					lowercase						:		Tuple   					=		BitModelTester(self							)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use PerceiverImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


